grafeo-langchain 0.1.1

LangChain graph store and vector store backed by GrafeoDB embedded graph database

grafeo-langchain

LangChain graph store and vector store backed by GrafeoDB — an embedded graph database with native vector search.

No servers, no Docker, no configuration. Just pip install and go.

Install

pip install grafeo-langchain

Quick Start

Knowledge Graph (GraphStore)

Store LLM-extracted triples and query them with GQL/Cypher:

from langchain_openai import ChatOpenAI
from langchain_experimental.graph_transformers import LLMGraphTransformer
from langchain_core.documents import Document
from grafeo_langchain import GrafeoGraphStore

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
transformer = LLMGraphTransformer(llm=llm)

documents = [
    Document(page_content="Alice works at Microsoft. Bob works at Google. Alice knows Bob."),
]
graph_documents = transformer.convert_to_graph_documents(documents)

store = GrafeoGraphStore(db_path="./knowledge.db")
store.add_graph_documents(graph_documents, include_source=True)

results = store.query("MATCH (p:Person)-[:WORKS_AT]->(c) RETURN p.node_id, c.node_id")
print(store.get_schema)

Vector + Graph Retrieval (GraphVectorStore)

Combine vector similarity search with graph traversal for Graph RAG:

from langchain_openai import OpenAIEmbeddings
from grafeo_langchain import GrafeoGraphVectorStore

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
store = GrafeoGraphVectorStore(
    embedding=embeddings,
    db_path="./doc_graph.db",
    embedding_dimensions=1536,
)

store.add_texts(
    texts=["Python is a programming language...", "Guido van Rossum...", "ABC influenced..."],
    metadatas=[
        {"id": "python", "__graph_links__": [{"target_id": "abc", "type": "INFLUENCED_BY"}]},
        {"id": "guido"},
        {"id": "abc", "__graph_links__": [{"target_id": "python", "type": "INFLUENCED"}]},
    ],
    ids=["python", "guido", "abc"],
)

# Standard vector search
docs = store.similarity_search("What programming languages exist?", k=2)

# Vector search + graph traversal
docs = store.traversal_search("What programming languages exist?", k=4, depth=2)

# MMR-diversified graph traversal
docs = store.mmr_traversal_search("programming history", k=4, depth=2, lambda_mult=0.7)

Why Grafeo?

Feature	Neo4j	Grafeo
Requires server	Yes (Docker/Cloud)	No (embedded, pip install)
GraphStore	Yes	Yes
GraphVectorStore	Community package	Built-in (native HNSW)
Query language	Cypher	GQL + Cypher + Gremlin
Graph algorithms	GDS plugin ($$$)	Built-in (PageRank, Louvain, ...)
Deployment	Docker container	Single .db file
Offline/edge	No	Yes

API Reference

GrafeoGraphStore

• GrafeoGraphStore(db_path=None) — in-memory or persistent graph store
• .add_graph_documents(docs, include_source=False) — ingest LLM-extracted graph documents
• .query(query, params=None) — execute GQL/Cypher queries
• .get_schema / .get_structured_schema — inspect the graph schema
• .refresh_schema() — refresh the cached schema
• .client — access the underlying GrafeoDB instance

GrafeoGraphVectorStore

• GrafeoGraphVectorStore(embedding, db_path=None, embedding_dimensions=1536) — vector store with graph links
• .add_texts(texts, metadatas=None, ids=None) — add documents with embeddings and optional graph links
• .similarity_search(query, k=4) — standard vector similarity search
• .traversal_search(query, k=4, depth=1) — vector search + graph traversal
• .mmr_traversal_search(query, k=4, depth=2, fetch_k=100, lambda_mult=0.5) — MMR-diversified traversal
• .from_texts(...) / .from_documents(...) — factory methods

Requirements

• Python 3.12+

License

Apache-2.0